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Applied AI Engineering Delivery Manager
N2P Systems
2 hours ago
Full-time
On-site
Job Title: Applied AI Engineering Delivery Manager
If you are considering sending an application, make sure to hit the apply button below after reading through the entire description. Tampa, FL or Nashville Experience:
10–12 Years Work Type:
Client-facing technology delivery and engineering management Travel:
0–20%, depending on client and project requirements Job Summary We are seeking an experienced
Applied AI Engineering Delivery Manager
to lead the delivery of complex, large-scale technology programs involving application modernization, system integration, AI-enabled engineering, and technology operations. The ideal candidate will combine strong program management, technical delivery expertise, people leadership, and hands-on experience with Generative AI and agentic engineering tools. This role focuses on improving delivery speed, software quality, operational efficiency, and business outcomes by integrating AI into the software development and delivery lifecycle. The successful candidate will partner with business stakeholders, engineering teams, vendors, and technology leaders to drive end-to-end delivery while establishing responsible AI practices and governance. Key Responsibilities 1. Technology Delivery and Program Management Manage the end-to-end lifecycle of large-scale application development, modernization, and transformation programs. Develop and manage project plans, schedules, budgets, resource allocations, and delivery milestones. Oversee application performance, reliability, scalability, and continuous improvement. Coordinate system upgrades, patches, enhancements, and integrations across enterprise technology environments. Manage dependencies between upstream, downstream, and external partner systems. Coordinate with clients, vendors, system integrators, and internal technology teams to ensure successful delivery. 2. AI-Enabled Engineering and Agentic Tools Integrate Generative AI and agentic tools into engineering workflows to improve productivity, quality, and delivery efficiency. Evaluate and pilot tools such as Claude Code, Claude Cowork, OpenAI Codex, GitHub Copilot, Cursor, and similar AI-assisted development platforms. Leverage AI tools for requirements analysis, effort estimation, project planning, documentation, code generation, testing, and release management. Use AI-assisted analytics and risk modeling to identify delivery risks, dependencies, and potential bottlenecks. Promote AI-enabled automation across the software development lifecycle while maintaining human oversight and accountability. Establish responsible AI guardrails, evaluation frameworks, and escalation processes to address hallucinations, bias, and model drift. 3. Stakeholder and Team Leadership Collaborate with business stakeholders to gather requirements and translate business needs into technical solutions. Serve as the primary liaison between business users, engineering teams, and delivery leadership. Lead, mentor, and develop application developers, analysts, and support teams. Manage resource allocation, workload planning, team performance, and delivery priorities. Foster collaboration, accountability, and continuous learning across cross-functional teams. Communicate project status, risks, issues, and delivery outcomes clearly to stakeholders. 4. Agile, DevOps, and Quality Engineering Drive Agile, DevOps, and continuous delivery practices across engineering teams. Encourage AI-assisted code reviews, automated testing, defect detection, and release management. Ensure accurate business logic implementation, data integrity, and reliable system integrations. Establish quality standards and delivery metrics to improve engineering outcomes. Promote continuous improvement and process optimization throughout the delivery lifecycle. 5. Application Operations and Service Management Oversee production support, application incidents, and timely issue resolution. Use AI-assisted root-cause analysis to accelerate incident triage and problem resolution. Establish and monitor Service Level Agreements (SLAs), service performance, and operational KPIs. Leverage monitoring, anomaly detection, and automated reporting to identify operational issues. Oversee dashboards, analytics, and management reporting to provide actionable delivery insights. Drive service reliability, cost efficiency, and operational excellence. Required Qualifications and Skills 10–12 years of experience in technology delivery, engineering management, program management, or technology consulting. Proven experience managing mid-to-large-scale technology engagements and complex application programs. Experience in application modernization, system integration, data integration, or technology operations. Demonstrated experience leading and mentoring cross-functional engineering and delivery teams. Hands-on exposure to Generative AI or agentic tools for software development and delivery activities. Understanding of AI-enabled software engineering workflows, including requirements analysis, code generation, testing, and documentation. Knowledge of responsible AI practices, including human-in-the-loop reviews, evaluation frameworks, hallucination mitigation, bias management, and model drift monitoring. Familiarity with Agile, DevOps, CI/CD, application lifecycle management, and software quality practices. Strong understanding of application performance, reliability, scalability, integrations, and production support. Experience with project planning, budgeting, risk management, stakeholder communication, and resource allocation. Bachelor's degree in Computer Science, Engineering, Business, or a related field. Preferred Qualifications Experience with agentic engineering tools such as Claude Code, Claude Cowork, OpenAI Codex, Cursor, or Visual Studio Code. Experience implementing AI-assisted development, testing, code review, and delivery automation. Familiarity with AI evaluation harnesses, monitoring frameworks, and governance practices. Experience managing client-facing technology consulting engagements. Knowledge of cloud platforms, enterprise application architectures, APIs, and data integration technologies. Required Professional Competencies Strong written and verbal communication skills. Excellent stakeholder management and relationship-building abilities. Strong leadership, mentoring, and decision-making skills. Ability to manage multiple priorities in a fast-paced environment. Strong analytical, problem-solving, and risk-management capabilities. Meticulous attention to detail and commitment to quality. Ability to work independently and collaboratively across teams. Proven ability to lead projects or workstreams and meet delivery deadlines. Additional Information Travel requirements: 0–20%, depending on client and project needs. Limited immigration sponsorship may be available. xsgimln Candidates should be comfortable working in collaborative, client-facing delivery environments.
If you are considering sending an application, make sure to hit the apply button below after reading through the entire description. Tampa, FL or Nashville Experience:
10–12 Years Work Type:
Client-facing technology delivery and engineering management Travel:
0–20%, depending on client and project requirements Job Summary We are seeking an experienced
Applied AI Engineering Delivery Manager
to lead the delivery of complex, large-scale technology programs involving application modernization, system integration, AI-enabled engineering, and technology operations. The ideal candidate will combine strong program management, technical delivery expertise, people leadership, and hands-on experience with Generative AI and agentic engineering tools. This role focuses on improving delivery speed, software quality, operational efficiency, and business outcomes by integrating AI into the software development and delivery lifecycle. The successful candidate will partner with business stakeholders, engineering teams, vendors, and technology leaders to drive end-to-end delivery while establishing responsible AI practices and governance. Key Responsibilities 1. Technology Delivery and Program Management Manage the end-to-end lifecycle of large-scale application development, modernization, and transformation programs. Develop and manage project plans, schedules, budgets, resource allocations, and delivery milestones. Oversee application performance, reliability, scalability, and continuous improvement. Coordinate system upgrades, patches, enhancements, and integrations across enterprise technology environments. Manage dependencies between upstream, downstream, and external partner systems. Coordinate with clients, vendors, system integrators, and internal technology teams to ensure successful delivery. 2. AI-Enabled Engineering and Agentic Tools Integrate Generative AI and agentic tools into engineering workflows to improve productivity, quality, and delivery efficiency. Evaluate and pilot tools such as Claude Code, Claude Cowork, OpenAI Codex, GitHub Copilot, Cursor, and similar AI-assisted development platforms. Leverage AI tools for requirements analysis, effort estimation, project planning, documentation, code generation, testing, and release management. Use AI-assisted analytics and risk modeling to identify delivery risks, dependencies, and potential bottlenecks. Promote AI-enabled automation across the software development lifecycle while maintaining human oversight and accountability. Establish responsible AI guardrails, evaluation frameworks, and escalation processes to address hallucinations, bias, and model drift. 3. Stakeholder and Team Leadership Collaborate with business stakeholders to gather requirements and translate business needs into technical solutions. Serve as the primary liaison between business users, engineering teams, and delivery leadership. Lead, mentor, and develop application developers, analysts, and support teams. Manage resource allocation, workload planning, team performance, and delivery priorities. Foster collaboration, accountability, and continuous learning across cross-functional teams. Communicate project status, risks, issues, and delivery outcomes clearly to stakeholders. 4. Agile, DevOps, and Quality Engineering Drive Agile, DevOps, and continuous delivery practices across engineering teams. Encourage AI-assisted code reviews, automated testing, defect detection, and release management. Ensure accurate business logic implementation, data integrity, and reliable system integrations. Establish quality standards and delivery metrics to improve engineering outcomes. Promote continuous improvement and process optimization throughout the delivery lifecycle. 5. Application Operations and Service Management Oversee production support, application incidents, and timely issue resolution. Use AI-assisted root-cause analysis to accelerate incident triage and problem resolution. Establish and monitor Service Level Agreements (SLAs), service performance, and operational KPIs. Leverage monitoring, anomaly detection, and automated reporting to identify operational issues. Oversee dashboards, analytics, and management reporting to provide actionable delivery insights. Drive service reliability, cost efficiency, and operational excellence. Required Qualifications and Skills 10–12 years of experience in technology delivery, engineering management, program management, or technology consulting. Proven experience managing mid-to-large-scale technology engagements and complex application programs. Experience in application modernization, system integration, data integration, or technology operations. Demonstrated experience leading and mentoring cross-functional engineering and delivery teams. Hands-on exposure to Generative AI or agentic tools for software development and delivery activities. Understanding of AI-enabled software engineering workflows, including requirements analysis, code generation, testing, and documentation. Knowledge of responsible AI practices, including human-in-the-loop reviews, evaluation frameworks, hallucination mitigation, bias management, and model drift monitoring. Familiarity with Agile, DevOps, CI/CD, application lifecycle management, and software quality practices. Strong understanding of application performance, reliability, scalability, integrations, and production support. Experience with project planning, budgeting, risk management, stakeholder communication, and resource allocation. Bachelor's degree in Computer Science, Engineering, Business, or a related field. Preferred Qualifications Experience with agentic engineering tools such as Claude Code, Claude Cowork, OpenAI Codex, Cursor, or Visual Studio Code. Experience implementing AI-assisted development, testing, code review, and delivery automation. Familiarity with AI evaluation harnesses, monitoring frameworks, and governance practices. Experience managing client-facing technology consulting engagements. Knowledge of cloud platforms, enterprise application architectures, APIs, and data integration technologies. Required Professional Competencies Strong written and verbal communication skills. Excellent stakeholder management and relationship-building abilities. Strong leadership, mentoring, and decision-making skills. Ability to manage multiple priorities in a fast-paced environment. Strong analytical, problem-solving, and risk-management capabilities. Meticulous attention to detail and commitment to quality. Ability to work independently and collaboratively across teams. Proven ability to lead projects or workstreams and meet delivery deadlines. Additional Information Travel requirements: 0–20%, depending on client and project needs. Limited immigration sponsorship may be available. xsgimln Candidates should be comfortable working in collaborative, client-facing delivery environments.